{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sai\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnet18()\n",
    "model = sai.models.resnet18(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnet34()\n",
    "model = sai.models.resnet34(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnet50()\n",
    "model = sai.models.resnet50(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnet101()\n",
    "model = sai.models.resnet101(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnet152()\n",
    "model = sai.models.resnet152(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnext50_32x4d()\n",
    "model = sai.models.resnext50_32x4d(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnext101_32x8d()\n",
    "model = sai.models.resnext101_32x8d(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnext101_32x8d_wsl()\n",
    "model = sai.models.resnext101_32x8d_wsl(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnext101_32x16d_wsl()\n",
    "model = sai.models.resnext101_32x16d_wsl(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnext101_32x32d_wsl()\n",
    "model = sai.models.resnext101_32x32d_wsl(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.resnext101_32x48d_wsl()\n",
    "model = sai.models.resnext101_32x48d_wsl(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.se_resnet50()\n",
    "model = sai.models.se_resnet50(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://www.flyai.com/m/se_resnet101-7e38fcc6.pth\" to C:\\Users\\whghc/.cache\\torch\\checkpoints\\se_resnet101-7e38fcc6.pth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model download succeeded !\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Error(s) in loading state_dict for SENet:\n\tMissing key(s) in state_dict: \"layer0.conv1.weight\", \"layer0.bn1.weight\", \"layer0.bn1.bias\", \"layer0.bn1.running_mean\", \"layer0.bn1.running_var\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.se_module.fc1.weight\", \"layer1.0.se_module.fc1.bias\", \"layer1.0.se_module.fc2.weight\", \"layer1.0.se_module.fc2.bias\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.se_module.fc1.weight\", \"layer1.1.se_module.fc1.bias\", \"layer1.1.se_module.fc2.weight\", \"layer1.1.se_module.fc2.bias\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.se_module.fc1.weight\", \"layer1.2.se_module.fc1.bias\", \"layer1.2.se_module.fc2.weight\", \"layer1.2.se_module.fc2.bias\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.se_module.fc1.weight\", \"layer2.0.se_module.fc1.bias\", \"layer2.0.se_module.fc2.weight\", \"layer2.0.se_module.fc2.bias\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.se_module.fc1.weight\", \"layer2.1.se_module.fc1.bias\", \"layer2.1.se_module.fc2.weight\", \"layer2.1.se_module.fc2.bias\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.se_module.fc1.weight\", \"layer2.2.se_module.fc1.bias\", \"layer2.2.se_module.fc2.weight\", \"layer2.2.se_module.fc2.bias\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.se_module.fc1.weight\", \"layer2.3.se_module.fc1.bias\", \"layer2.3.se_module.fc2.weight\", \"layer2.3.se_module.fc2.bias\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.se_module.fc1.weight\", \"layer3.0.se_module.fc1.bias\", \"layer3.0.se_module.fc2.weight\", \"layer3.0.se_module.fc2.bias\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.se_module.fc1.weight\", \"layer3.1.se_module.fc1.bias\", \"layer3.1.se_module.fc2.weight\", \"layer3.1.se_module.fc2.bias\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.se_module.fc1.weight\", \"layer3.2.se_module.fc1.bias\", \"layer3.2.se_module.fc2.weight\", \"layer3.2.se_module.fc2.bias\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.se_module.fc1.weight\", \"layer3.3.se_module.fc1.bias\", \"layer3.3.se_module.fc2.weight\", \"layer3.3.se_module.fc2.bias\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.se_module.fc1.weight\", \"layer3.4.se_module.fc1.bias\", \"layer3.4.se_module.fc2.weight\", \"layer3.4.se_module.fc2.bias\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.se_module.fc1.weight\", \"layer3.5.se_module.fc1.bias\", \"layer3.5.se_module.fc2.weight\", \"layer3.5.se_module.fc2.bias\", \"layer3.6.conv1.weight\", \"layer3.6.bn1.weight\", \"layer3.6.bn1.bias\", \"layer3.6.bn1.running_mean\", \"layer3.6.bn1.running_var\", \"layer3.6.conv2.weight\", \"layer3.6.bn2.weight\", \"layer3.6.bn2.bias\", \"layer3.6.bn2.running_mean\", \"layer3.6.bn2.running_var\", \"layer3.6.conv3.weight\", \"layer3.6.bn3.weight\", \"layer3.6.bn3.bias\", \"layer3.6.bn3.running_mean\", \"layer3.6.bn3.running_var\", \"layer3.6.se_module.fc1.weight\", \"layer3.6.se_module.fc1.bias\", \"layer3.6.se_module.fc2.weight\", \"layer3.6.se_module.fc2.bias\", \"layer3.7.conv1.weight\", \"layer3.7.bn1.weight\", \"layer3.7.bn1.bias\", \"layer3.7.bn1.running_mean\", \"layer3.7.bn1.running_var\", \"layer3.7.conv2.weight\", \"layer3.7.bn2.weight\", \"layer3.7.bn2.bias\", \"layer3.7.bn2.running_mean\", \"layer3.7.bn2.running_var\", \"layer3.7.conv3.weight\", \"layer3.7.bn3.weight\", \"layer3.7.bn3.bias\", \"layer3.7.bn3.running_mean\", \"layer3.7.bn3.running_var\", \"layer3.7.se_module.fc1.weight\", \"layer3.7.se_module.fc1.bias\", \"layer3.7.se_module.fc2.weight\", \"layer3.7.se_module.fc2.bias\", \"layer3.8.conv1.weight\", \"layer3.8.bn1.weight\", \"layer3.8.bn1.bias\", \"layer3.8.bn1.running_mean\", \"layer3.8.bn1.running_var\", \"layer3.8.conv2.weight\", \"layer3.8.bn2.weight\", \"layer3.8.bn2.bias\", \"layer3.8.bn2.running_mean\", \"layer3.8.bn2.running_var\", \"layer3.8.conv3.weight\", \"layer3.8.bn3.weight\", \"layer3.8.bn3.bias\", \"layer3.8.bn3.running_mean\", \"layer3.8.bn3.running_var\", \"layer3.8.se_module.fc1.weight\", \"layer3.8.se_module.fc1.bias\", \"layer3.8.se_module.fc2.weight\", \"layer3.8.se_module.fc2.bias\", \"layer3.9.conv1.weight\", \"layer3.9.bn1.weight\", \"layer3.9.bn1.bias\", \"layer3.9.bn1.running_mean\", \"layer3.9.bn1.running_var\", \"layer3.9.conv2.weight\", \"layer3.9.bn2.weight\", \"layer3.9.bn2.bias\", \"layer3.9.bn2.running_mean\", \"layer3.9.bn2.running_var\", \"layer3.9.conv3.weight\", \"layer3.9.bn3.weight\", \"layer3.9.bn3.bias\", \"layer3.9.bn3.running_mean\", \"layer3.9.bn3.running_var\", \"layer3.9.se_module.fc1.weight\", \"layer3.9.se_module.fc1.bias\", \"layer3.9.se_module.fc2.weight\", \"layer3.9.se_module.fc2.bias\", \"layer3.10.conv1.weight\", \"layer3.10.bn1.weight\", \"layer3.10.bn1.bias\", \"layer3.10.bn1.running_mean\", \"layer3.10.bn1.running_var\", \"layer3.10.conv2.weight\", \"layer3.10.bn2.weight\", \"layer3.10.bn2.bias\", \"layer3.10.bn2.running_mean\", \"layer3.10.bn2.running_var\", \"layer3.10.conv3.weight\", \"layer3.10.bn3.weight\", \"layer3.10.bn3.bias\", \"layer3.10.bn3.running_mean\", \"layer3.10.bn3.running_var\", \"layer3.10.se_module.fc1.weight\", \"layer3.10.se_module.fc1.bias\", \"layer3.10.se_module.fc2.weight\", \"layer3.10.se_module.fc2.bias\", \"layer3.11.conv1.weight\", \"layer3.11.bn1.weight\", \"layer3.11.bn1.bias\", \"layer3.11.bn1.running_mean\", \"layer3.11.bn1.running_var\", \"layer3.11.conv2.weight\", \"layer3.11.bn2.weight\", \"layer3.11.bn2.bias\", \"layer3.11.bn2.running_mean\", \"layer3.11.bn2.running_var\", \"layer3.11.conv3.weight\", \"layer3.11.bn3.weight\", \"layer3.11.bn3.bias\", \"layer3.11.bn3.running_mean\", \"layer3.11.bn3.running_var\", \"layer3.11.se_module.fc1.weight\", \"layer3.11.se_module.fc1.bias\", \"layer3.11.se_module.fc2.weight\", \"layer3.11.se_module.fc2.bias\", \"layer3.12.conv1.weight\", \"layer3.12.bn1.weight\", \"layer3.12.bn1.bias\", \"layer3.12.bn1.running_mean\", \"layer3.12.bn1.running_var\", \"layer3.12.conv2.weight\", \"layer3.12.bn2.weight\", \"layer3.12.bn2.bias\", \"layer3.12.bn2.running_mean\", \"layer3.12.bn2.running_var\", \"layer3.12.conv3.weight\", \"layer3.12.bn3.weight\", \"layer3.12.bn3.bias\", \"layer3.12.bn3.running_mean\", \"layer3.12.bn3.running_var\", \"layer3.12.se_module.fc1.weight\", \"layer3.12.se_module.fc1.bias\", \"layer3.12.se_module.fc2.weight\", \"layer3.12.se_module.fc2.bias\", \"layer3.13.conv1.weight\", \"layer3.13.bn1.weight\", \"layer3.13.bn1.bias\", \"layer3.13.bn1.running_mean\", \"layer3.13.bn1.running_var\", \"layer3.13.conv2.weight\", \"layer3.13.bn2.weight\", \"layer3.13.bn2.bias\", \"layer3.13.bn2.running_mean\", \"layer3.13.bn2.running_var\", \"layer3.13.conv3.weight\", \"layer3.13.bn3.weight\", \"layer3.13.bn3.bias\", \"layer3.13.bn3.running_mean\", \"layer3.13.bn3.running_var\", \"layer3.13.se_module.fc1.weight\", \"layer3.13.se_module.fc1.bias\", \"layer3.13.se_module.fc2.weight\", \"layer3.13.se_module.fc2.bias\", \"layer3.14.conv1.weight\", \"layer3.14.bn1.weight\", \"layer3.14.bn1.bias\", \"layer3.14.bn1.running_mean\", \"layer3.14.bn1.running_var\", \"layer3.14.conv2.weight\", \"layer3.14.bn2.weight\", \"layer3.14.bn2.bias\", \"layer3.14.bn2.running_mean\", \"layer3.14.bn2.running_var\", \"layer3.14.conv3.weight\", \"layer3.14.bn3.weight\", \"layer3.14.bn3.bias\", \"layer3.14.bn3.running_mean\", \"layer3.14.bn3.running_var\", \"layer3.14.se_module.fc1.weight\", \"layer3.14.se_module.fc1.bias\", \"layer3.14.se_module.fc2.weight\", \"layer3.14.se_module.fc2.bias\", \"layer3.15.conv1.weight\", \"layer3.15.bn1.weight\", \"layer3.15.bn1.bias\", \"layer3.15.bn1.running_mean\", \"layer3.15.bn1.running_var\", \"layer3.15.conv2.weight\", \"layer3.15.bn2.weight\", \"layer3.15.bn2.bias\", \"layer3.15.bn2.running_mean\", \"layer3.15.bn2.running_var\", \"layer3.15.conv3.weight\", \"layer3.15.bn3.weight\", \"layer3.15.bn3.bias\", \"layer3.15.bn3.running_mean\", \"layer3.15.bn3.running_var\", \"layer3.15.se_module.fc1.weight\", \"layer3.15.se_module.fc1.bias\", \"layer3.15.se_module.fc2.weight\", \"layer3.15.se_module.fc2.bias\", \"layer3.16.conv1.weight\", \"layer3.16.bn1.weight\", \"layer3.16.bn1.bias\", \"layer3.16.bn1.running_mean\", \"layer3.16.bn1.running_var\", \"layer3.16.conv2.weight\", \"layer3.16.bn2.weight\", \"layer3.16.bn2.bias\", \"layer3.16.bn2.running_mean\", \"layer3.16.bn2.running_var\", \"layer3.16.conv3.weight\", \"layer3.16.bn3.weight\", \"layer3.16.bn3.bias\", \"layer3.16.bn3.running_mean\", \"layer3.16.bn3.running_var\", \"layer3.16.se_module.fc1.weight\", \"layer3.16.se_module.fc1.bias\", \"layer3.16.se_module.fc2.weight\", \"layer3.16.se_module.fc2.bias\", \"layer3.17.conv1.weight\", \"layer3.17.bn1.weight\", \"layer3.17.bn1.bias\", \"layer3.17.bn1.running_mean\", \"layer3.17.bn1.running_var\", \"layer3.17.conv2.weight\", \"layer3.17.bn2.weight\", \"layer3.17.bn2.bias\", \"layer3.17.bn2.running_mean\", \"layer3.17.bn2.running_var\", \"layer3.17.conv3.weight\", \"layer3.17.bn3.weight\", \"layer3.17.bn3.bias\", \"layer3.17.bn3.running_mean\", \"layer3.17.bn3.running_var\", \"layer3.17.se_module.fc1.weight\", \"layer3.17.se_module.fc1.bias\", \"layer3.17.se_module.fc2.weight\", \"layer3.17.se_module.fc2.bias\", \"layer3.18.conv1.weight\", \"layer3.18.bn1.weight\", \"layer3.18.bn1.bias\", \"layer3.18.bn1.running_mean\", \"layer3.18.bn1.running_var\", \"layer3.18.conv2.weight\", \"layer3.18.bn2.weight\", \"layer3.18.bn2.bias\", \"layer3.18.bn2.running_mean\", \"layer3.18.bn2.running_var\", \"layer3.18.conv3.weight\", \"layer3.18.bn3.weight\", \"layer3.18.bn3.bias\", \"layer3.18.bn3.running_mean\", \"layer3.18.bn3.running_var\", \"layer3.18.se_module.fc1.weight\", \"layer3.18.se_module.fc1.bias\", \"layer3.18.se_module.fc2.weight\", \"layer3.18.se_module.fc2.bias\", \"layer3.19.conv1.weight\", \"layer3.19.bn1.weight\", \"layer3.19.bn1.bias\", \"layer3.19.bn1.running_mean\", \"layer3.19.bn1.running_var\", \"layer3.19.conv2.weight\", \"layer3.19.bn2.weight\", \"layer3.19.bn2.bias\", \"layer3.19.bn2.running_mean\", \"layer3.19.bn2.running_var\", \"layer3.19.conv3.weight\", \"layer3.19.bn3.weight\", \"layer3.19.bn3.bias\", \"layer3.19.bn3.running_mean\", \"layer3.19.bn3.running_var\", \"layer3.19.se_module.fc1.weight\", \"layer3.19.se_module.fc1.bias\", \"layer3.19.se_module.fc2.weight\", \"layer3.19.se_module.fc2.bias\", \"layer3.20.conv1.weight\", \"layer3.20.bn1.weight\", \"layer3.20.bn1.bias\", \"layer3.20.bn1.running_mean\", \"layer3.20.bn1.running_var\", \"layer3.20.conv2.weight\", \"layer3.20.bn2.weight\", \"layer3.20.bn2.bias\", \"layer3.20.bn2.running_mean\", \"layer3.20.bn2.running_var\", \"layer3.20.conv3.weight\", \"layer3.20.bn3.weight\", \"layer3.20.bn3.bias\", \"layer3.20.bn3.running_mean\", \"layer3.20.bn3.running_var\", \"layer3.20.se_module.fc1.weight\", \"layer3.20.se_module.fc1.bias\", \"layer3.20.se_module.fc2.weight\", \"layer3.20.se_module.fc2.bias\", \"layer3.21.conv1.weight\", \"layer3.21.bn1.weight\", \"layer3.21.bn1.bias\", \"layer3.21.bn1.running_mean\", \"layer3.21.bn1.running_var\", \"layer3.21.conv2.weight\", \"layer3.21.bn2.weight\", \"layer3.21.bn2.bias\", \"layer3.21.bn2.running_mean\", \"layer3.21.bn2.running_var\", \"layer3.21.conv3.weight\", \"layer3.21.bn3.weight\", \"layer3.21.bn3.bias\", \"layer3.21.bn3.running_mean\", \"layer3.21.bn3.running_var\", \"layer3.21.se_module.fc1.weight\", \"layer3.21.se_module.fc1.bias\", \"layer3.21.se_module.fc2.weight\", \"layer3.21.se_module.fc2.bias\", \"layer3.22.conv1.weight\", \"layer3.22.bn1.weight\", \"layer3.22.bn1.bias\", \"layer3.22.bn1.running_mean\", \"layer3.22.bn1.running_var\", \"layer3.22.conv2.weight\", \"layer3.22.bn2.weight\", \"layer3.22.bn2.bias\", \"layer3.22.bn2.running_mean\", \"layer3.22.bn2.running_var\", \"layer3.22.conv3.weight\", \"layer3.22.bn3.weight\", \"layer3.22.bn3.bias\", \"layer3.22.bn3.running_mean\", \"layer3.22.bn3.running_var\", \"layer3.22.se_module.fc1.weight\", \"layer3.22.se_module.fc1.bias\", \"layer3.22.se_module.fc2.weight\", \"layer3.22.se_module.fc2.bias\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.se_module.fc1.weight\", \"layer4.0.se_module.fc1.bias\", \"layer4.0.se_module.fc2.weight\", \"layer4.0.se_module.fc2.bias\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.se_module.fc1.weight\", \"layer4.1.se_module.fc1.bias\", \"layer4.1.se_module.fc2.weight\", \"layer4.1.se_module.fc2.bias\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.se_module.fc1.weight\", \"layer4.2.se_module.fc1.bias\", \"layer4.2.se_module.fc2.weight\", \"layer4.2.se_module.fc2.bias\", \"last_linear.weight\", \"last_linear.bias\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.weight\", \"bn1.bias\", \"fc.weight\", \"fc.bias\". \n\tsize mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 1, 1]).\n\tsize mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.0.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).\n\tsize mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-37b61694f1a9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msai\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mse_resnet101\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msai\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mse_resnet101\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpretrained\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\sai-0.0.2-py3.7.egg\\sai\\models\\senet.py\u001b[0m in \u001b[0;36mse_resnet101\u001b[1;34m(num_classes, pretrained)\u001b[0m\n\u001b[0;32m    412\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m         \u001b[0msettings\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpretrained_settings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'se_resnet101'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'imagenet'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 414\u001b[1;33m         \u001b[0minitialize_pretrained_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    415\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\sai-0.0.2-py3.7.egg\\sai\\models\\senet.py\u001b[0m in \u001b[0;36minitialize_pretrained_model\u001b[1;34m(model, num_classes, settings)\u001b[0m\n\u001b[0;32m    377\u001b[0m         'num_classes should be {}, but is {}'.format(\n\u001b[0;32m    378\u001b[0m             settings['num_classes'], num_classes)\n\u001b[1;32m--> 379\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'url'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    380\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_space\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input_space'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input_size'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[1;34m(self, state_dict, strict)\u001b[0m\n\u001b[0;32m    828\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    829\u001b[0m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[1;32m--> 830\u001b[1;33m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[0m\u001b[0;32m    831\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_IncompatibleKeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munexpected_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for SENet:\n\tMissing key(s) in state_dict: \"layer0.conv1.weight\", \"layer0.bn1.weight\", \"layer0.bn1.bias\", \"layer0.bn1.running_mean\", \"layer0.bn1.running_var\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.se_module.fc1.weight\", \"layer1.0.se_module.fc1.bias\", \"layer1.0.se_module.fc2.weight\", \"layer1.0.se_module.fc2.bias\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.se_module.fc1.weight\", \"layer1.1.se_module.fc1.bias\", \"layer1.1.se_module.fc2.weight\", \"layer1.1.se_module.fc2.bias\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.se_module.fc1.weight\", \"layer1.2.se_module.fc1.bias\", \"layer1.2.se_module.fc2.weight\", \"layer1.2.se_module.fc2.bias\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.se_module.fc1.weight\", \"layer2.0.se_module.fc1.bias\", \"layer2.0.se_module.fc2.weight\", \"layer2.0.se_module.fc2.bias\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.se_module.fc1.weight\", \"layer2.1.se_module.fc1.bias\", \"layer2.1.se_module.fc2.weight\", \"layer2.1.se_module.fc2.bias\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.se_module.fc1.weight\", \"layer2.2.se_module.fc1.bias\", \"layer2.2.se_module.fc2.weight\", \"layer2.2.se_module.fc2.bias\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.se_module.fc1.weight\", \"layer2.3.se_module.fc1.bias\", \"layer2.3.se_module.fc2.weight\", \"layer2.3.se_module.fc2.bias\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.se_module.fc1.weight\", \"layer3.0.se_module.fc1.bias\", \"layer3.0.se_module.fc2.weight\", \"layer3.0.se_module.fc2.bias\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.se_module.fc1.weight\", \"layer3.1.se_module.fc1.bias\", \"layer3.1.se_module.fc2.weight\", \"layer3.1.se_module.fc2.bias\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.se_module.fc1.weight\", \"layer3.2.se_module.fc1.bias\", \"layer3.2.se_module.fc2.weight\", \"layer3.2.se_module.fc2.bias\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.se_module.fc1.weight\", \"layer3.3.se_module.fc1.bias\", \"layer3.3.se_module.fc2.weight\", \"layer3.3.se_module.fc2.bias\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.se_module.fc1.weight\", \"layer3.4.se_module.fc1.bias\", \"layer3.4.se_module.fc2.weight\", \"layer3.4.se_module.fc2.bias\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.se_module.fc1.weight\", \"layer3.5.se_module.fc1.bias\", \"layer3.5.se_module.fc2.weight\", \"layer3.5.se_module.fc2.bias\", \"layer3.6.conv1.weight\", \"layer3.6.bn1.weight\", \"layer3.6.bn1.bias\", \"layer3.6.bn1.running_mean\", \"layer3.6.bn1.running_var\", \"layer3.6.conv2.weight\", \"layer3.6.bn2.weight\", \"layer3.6.bn2.bias\", \"layer3.6.bn2.running_mean\", \"layer3.6.bn2.running_var\", \"layer3.6.conv3.weight\", \"layer3.6.bn3.weight\", \"layer3.6.bn3.bias\", \"layer3.6.bn3.running_mean\", \"layer3.6.bn3.running_var\", \"layer3.6.se_module.fc1.weight\", \"layer3.6.se_module.fc1.bias\", \"layer3.6.se_module.fc2.weight\", \"layer3.6.se_module.fc2.bias\", \"layer3.7.conv1.weight\", \"layer3.7.bn1.weight\", \"layer3.7.bn1.bias\", \"layer3.7.bn1.running_mean\", \"layer3.7.bn1.running_var\", \"layer3.7.conv2.weight\", \"layer3.7.bn2.weight\", \"layer3.7.bn2.bias\", \"layer3.7.bn2.running_mean\", \"layer3.7.bn2.running_var\", \"layer3.7.conv3.weight\", \"layer3.7.bn3.weight\", \"layer3.7.bn3.bias\", \"layer3.7.bn3.running_mean\", \"layer3.7.bn3.running_var\", \"layer3.7.se_module.fc1.weight\", \"layer3.7.se_module.fc1.bias\", \"layer3.7.se_module.fc2.weight\", \"layer3.7.se_module.fc2.bias\", \"layer3.8.conv1.weight\", \"layer3.8.bn1.weight\", \"layer3.8.bn1.bias\", \"layer3.8.bn1.running_mean\", \"layer3.8.bn1.running_var\", \"layer3.8.conv2.weight\", \"layer3.8.bn2.weight\", \"layer3.8.bn2.bias\", \"layer3.8.bn2.running_mean\", \"layer3.8.bn2.running_var\", \"layer3.8.conv3.weight\", \"layer3.8.bn3.weight\", \"layer3.8.bn3.bias\", \"layer3.8.bn3.running_mean\", \"layer3.8.bn3.running_var\", \"layer3.8.se_module.fc1.weight\", \"layer3.8.se_module.fc1.bias\", \"layer3.8.se_module.fc2.weight\", \"layer3.8.se_module.fc2.bias\", \"layer3.9.conv1.weight\", \"layer3.9.bn1.weight\", \"layer3.9.bn1.bias\", \"layer3.9.bn1.running_mean\", \"layer3.9.bn1.running_var\", \"layer3.9.conv2.weight\", \"layer3.9.bn2.weight\", \"layer3.9.bn2.bias\", \"layer3.9.bn2.running_mean\", \"layer3.9.bn2.running_var\", \"layer3.9.conv3.weight\", \"layer3.9.bn3.weight\", \"layer3.9.bn3.bias\", \"layer3.9.bn3.running_mean\", \"layer3.9.bn3.running_var\", \"layer3.9.se_module.fc1.weight\", \"layer3.9.se_module.fc1.bias\", \"layer3.9.se_module.fc2.weight\", \"layer3.9.se_module.fc2.bias\", \"layer3.10.conv1.weight\", \"layer3.10.bn1.weight\", \"layer3.10.bn1.bias\", \"layer3.10.bn1.running_mean\", \"layer3.10.bn1.running_var\", \"layer3.10.conv2.weight\", \"layer3.10.bn2.weight\", \"layer3.10.bn2.bias\", \"layer3.10.bn2.running_mean\", \"layer3.10.bn2.running_var\", \"layer3.10.conv3.weight\", \"layer3.10.bn3.weight\", \"layer3.10.bn3.bias\", \"layer3.10.bn3.running_mean\", \"layer3.10.bn3.running_var\", \"layer3.10.se_module.fc1.weight\", \"layer3.10.se_module.fc1.bias\", \"layer3.10.se_module.fc2.weight\", \"layer3.10.se_module.fc2.bias\", \"layer3.11.conv1.weight\", \"layer3.11.bn1.weight\", \"layer3.11.bn1.bias\", \"layer3.11.bn1.running_mean\", \"layer3.11.bn1.running_var\", \"layer3.11.conv2.weight\", \"layer3.11.bn2.weight\", \"layer3.11.bn2.bias\", \"layer3.11.bn2.running_mean\", \"layer3.11.bn2.running_var\", \"layer3.11.conv3.weight\", \"layer3.11.bn3.weight\", \"layer3.11.bn3.bias\", \"layer3.11.bn3.running_mean\", \"layer3.11.bn3.running_var\", \"layer3.11.se_module.fc1.weight\", \"layer3.11.se_module.fc1.bias\", \"layer3.11.se_module.fc2.weight\", \"layer3.11.se_module.fc2.bias\", \"layer3.12.conv1.weight\", \"layer3.12.bn1.weight\", \"layer3.12.bn1.bias\", \"layer3.12.bn1.running_mean\", \"layer3.12.bn1.running_var\", \"layer3.12.conv2.weight\", \"layer3.12.bn2.weight\", \"layer3.12.bn2.bias\", \"layer3.12.bn2.running_mean\", \"layer3.12.bn2.running_var\", \"layer3.12.conv3.weight\", \"layer3.12.bn3.weight\", \"layer3.12.bn3.bias\", \"layer3.12.bn3.running_mean\", \"layer3.12.bn3.running_var\", \"layer3.12.se_module.fc1.weight\", \"layer3.12.se_module.fc1.bias\", \"layer3.12.se_module.fc2.weight\", \"layer3.12.se_module.fc2.bias\", \"layer3.13.conv1.weight\", \"layer3.13.bn1.weight\", \"layer3.13.bn1.bias\", \"layer3.13.bn1.running_mean\", \"layer3.13.bn1.running_var\", \"layer3.13.conv2.weight\", \"layer3.13.bn2.weight\", \"layer3.13.bn2.bias\", \"layer3.13.bn2.running_mean\", \"layer3.13.bn2.running_var\", \"layer3.13.conv3.weight\", \"layer3.13.bn3.weight\", \"layer3.13.bn3.bias\", \"layer3.13.bn3.running_mean\", \"layer3.13.bn3.running_var\", \"layer3.13.se_module.fc1.weight\", \"layer3.13.se_module.fc1.bias\", \"layer3.13.se_module.fc2.weight\", \"layer3.13.se_module.fc2.bias\", \"layer3.14.conv1.weight\", \"layer3.14.bn1.weight\", \"layer3.14.bn1.bias\", \"layer3.14.bn1.running_mean\", \"layer3.14.bn1.running_var\", \"layer3.14.conv2.weight\", \"layer3.14.bn2.weight\", \"layer3.14.bn2.bias\", \"layer3.14.bn2.running_mean\", \"layer3.14.bn2.running_var\", \"layer3.14.conv3.weight\", \"layer3.14.bn3.weight\", \"layer3.14.bn3.bias\", \"layer3.14.bn3.running_mean\", \"layer3.14.bn3.running_var\", \"layer3.14.se_module.fc1.weight\", \"layer3.14.se_module.fc1.bias\", \"layer3.14.se_module.fc2.weight\", \"layer3.14.se_module.fc2.bias\", \"layer3.15.conv1.weight\", \"layer3.15.bn1.weight\", \"layer3.15.bn1.bias\", \"layer3.15.bn1.running_mean\", \"layer3.15.bn1.running_var\", \"layer3.15.conv2.weight\", \"layer3.15.bn2.weight\", \"layer3.15.bn2.bias\", \"layer3.15.bn2.running_mean\", \"layer3.15.bn2.running_var\", \"layer3.15.conv3.weight\", \"layer3.15.bn3.weight\", \"layer3.15.bn3.bias\", \"layer3.15.bn3.running_mean\", \"layer3.15.bn3.running_var\", \"layer3.15.se_module.fc1.weight\", \"layer3.15.se_module.fc1.bias\", \"layer3.15.se_module.fc2.weight\", \"layer3.15.se_module.fc2.bias\", \"layer3.16.conv1.weight\", \"layer3.16.bn1.weight\", \"layer3.16.bn1.bias\", \"layer3.16.bn1.running_mean\", \"layer3.16.bn1.running_var\", \"layer3.16.conv2.weight\", \"layer3.16.bn2.weight\", \"layer3.16.bn2.bias\", \"layer3.16.bn2.running_mean\", \"layer3.16.bn2.running_var\", \"layer3.16.conv3.weight\", \"layer3.16.bn3.weight\", \"layer3.16.bn3.bias\", \"layer3.16.bn3.running_mean\", \"layer3.16.bn3.running_var\", \"layer3.16.se_module.fc1.weight\", \"layer3.16.se_module.fc1.bias\", \"layer3.16.se_module.fc2.weight\", \"layer3.16.se_module.fc2.bias\", \"layer3.17.conv1.weight\", \"layer3.17.bn1.weight\", \"layer3.17.bn1.bias\", \"layer3.17.bn1.running_mean\", \"layer3.17.bn1.running_var\", \"layer3.17.conv2.weight\", \"layer3.17.bn2.weight\", \"layer3.17.bn2.bias\", \"layer3.17.bn2.running_mean\", \"layer3.17.bn2.running_var\", \"layer3.17.conv3.weight\", \"layer3.17.bn3.weight\", \"layer3.17.bn3.bias\", \"layer3.17.bn3.running_mean\", \"layer3.17.bn3.running_var\", \"layer3.17.se_module.fc1.weight\", \"layer3.17.se_module.fc1.bias\", \"layer3.17.se_module.fc2.weight\", \"layer3.17.se_module.fc2.bias\", \"layer3.18.conv1.weight\", \"layer3.18.bn1.weight\", \"layer3.18.bn1.bias\", \"layer3.18.bn1.running_mean\", \"layer3.18.bn1.running_var\", \"layer3.18.conv2.weight\", \"layer3.18.bn2.weight\", \"layer3.18.bn2.bias\", \"layer3.18.bn2.running_mean\", \"layer3.18.bn2.running_var\", \"layer3.18.conv3.weight\", \"layer3.18.bn3.weight\", \"layer3.18.bn3.bias\", \"layer3.18.bn3.running_mean\", \"layer3.18.bn3.running_var\", \"layer3.18.se_module.fc1.weight\", \"layer3.18.se_module.fc1.bias\", \"layer3.18.se_module.fc2.weight\", \"layer3.18.se_module.fc2.bias\", \"layer3.19.conv1.weight\", \"layer3.19.bn1.weight\", \"layer3.19.bn1.bias\", \"layer3.19.bn1.running_mean\", \"layer3.19.bn1.running_var\", \"layer3.19.conv2.weight\", \"layer3.19.bn2.weight\", \"layer3.19.bn2.bias\", \"layer3.19.bn2.running_mean\", \"layer3.19.bn2.running_var\", \"layer3.19.conv3.weight\", \"layer3.19.bn3.weight\", \"layer3.19.bn3.bias\", \"layer3.19.bn3.running_mean\", \"layer3.19.bn3.running_var\", \"layer3.19.se_module.fc1.weight\", \"layer3.19.se_module.fc1.bias\", \"layer3.19.se_module.fc2.weight\", \"layer3.19.se_module.fc2.bias\", \"layer3.20.conv1.weight\", \"layer3.20.bn1.weight\", \"layer3.20.bn1.bias\", \"layer3.20.bn1.running_mean\", \"layer3.20.bn1.running_var\", \"layer3.20.conv2.weight\", \"layer3.20.bn2.weight\", \"layer3.20.bn2.bias\", \"layer3.20.bn2.running_mean\", \"layer3.20.bn2.running_var\", \"layer3.20.conv3.weight\", \"layer3.20.bn3.weight\", \"layer3.20.bn3.bias\", \"layer3.20.bn3.running_mean\", \"layer3.20.bn3.running_var\", \"layer3.20.se_module.fc1.weight\", \"layer3.20.se_module.fc1.bias\", \"layer3.20.se_module.fc2.weight\", \"layer3.20.se_module.fc2.bias\", \"layer3.21.conv1.weight\", \"layer3.21.bn1.weight\", \"layer3.21.bn1.bias\", \"layer3.21.bn1.running_mean\", \"layer3.21.bn1.running_var\", \"layer3.21.conv2.weight\", \"layer3.21.bn2.weight\", \"layer3.21.bn2.bias\", \"layer3.21.bn2.running_mean\", \"layer3.21.bn2.running_var\", \"layer3.21.conv3.weight\", \"layer3.21.bn3.weight\", \"layer3.21.bn3.bias\", \"layer3.21.bn3.running_mean\", \"layer3.21.bn3.running_var\", \"layer3.21.se_module.fc1.weight\", \"layer3.21.se_module.fc1.bias\", \"layer3.21.se_module.fc2.weight\", \"layer3.21.se_module.fc2.bias\", \"layer3.22.conv1.weight\", \"layer3.22.bn1.weight\", \"layer3.22.bn1.bias\", \"layer3.22.bn1.running_mean\", \"layer3.22.bn1.running_var\", \"layer3.22.conv2.weight\", \"layer3.22.bn2.weight\", \"layer3.22.bn2.bias\", \"layer3.22.bn2.running_mean\", \"layer3.22.bn2.running_var\", \"layer3.22.conv3.weight\", \"layer3.22.bn3.weight\", \"layer3.22.bn3.bias\", \"layer3.22.bn3.running_mean\", \"layer3.22.bn3.running_var\", \"layer3.22.se_module.fc1.weight\", \"layer3.22.se_module.fc1.bias\", \"layer3.22.se_module.fc2.weight\", \"layer3.22.se_module.fc2.bias\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.se_module.fc1.weight\", \"layer4.0.se_module.fc1.bias\", \"layer4.0.se_module.fc2.weight\", \"layer4.0.se_module.fc2.bias\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.se_module.fc1.weight\", \"layer4.1.se_module.fc1.bias\", \"layer4.1.se_module.fc2.weight\", \"layer4.1.se_module.fc2.bias\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.se_module.fc1.weight\", \"layer4.2.se_module.fc1.bias\", \"layer4.2.se_module.fc2.weight\", \"layer4.2.se_module.fc2.bias\", \"last_linear.weight\", \"last_linear.bias\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.weight\", \"bn1.bias\", \"fc.weight\", \"fc.bias\". \n\tsize mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 1, 1]).\n\tsize mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.0.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).\n\tsize mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1])."
     ]
    }
   ],
   "source": [
    "model = sai.models.se_resnet101()\n",
    "model = sai.models.se_resnet101(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://www.flyai.com/m/se_resnet152-d17c99b7.pth\" to C:\\Users\\whghc/.cache\\torch\\checkpoints\\se_resnet152-d17c99b7.pth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model download succeeded !\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Error(s) in loading state_dict for SENet:\n\tMissing key(s) in state_dict: \"layer0.conv1.weight\", \"layer0.bn1.weight\", \"layer0.bn1.bias\", \"layer0.bn1.running_mean\", \"layer0.bn1.running_var\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.se_module.fc1.weight\", \"layer1.0.se_module.fc1.bias\", \"layer1.0.se_module.fc2.weight\", \"layer1.0.se_module.fc2.bias\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.se_module.fc1.weight\", \"layer1.1.se_module.fc1.bias\", \"layer1.1.se_module.fc2.weight\", \"layer1.1.se_module.fc2.bias\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.se_module.fc1.weight\", \"layer1.2.se_module.fc1.bias\", \"layer1.2.se_module.fc2.weight\", \"layer1.2.se_module.fc2.bias\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.se_module.fc1.weight\", \"layer2.0.se_module.fc1.bias\", \"layer2.0.se_module.fc2.weight\", \"layer2.0.se_module.fc2.bias\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.se_module.fc1.weight\", \"layer2.1.se_module.fc1.bias\", \"layer2.1.se_module.fc2.weight\", \"layer2.1.se_module.fc2.bias\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.se_module.fc1.weight\", \"layer2.2.se_module.fc1.bias\", \"layer2.2.se_module.fc2.weight\", \"layer2.2.se_module.fc2.bias\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.se_module.fc1.weight\", \"layer2.3.se_module.fc1.bias\", \"layer2.3.se_module.fc2.weight\", \"layer2.3.se_module.fc2.bias\", \"layer2.4.conv1.weight\", \"layer2.4.bn1.weight\", \"layer2.4.bn1.bias\", \"layer2.4.bn1.running_mean\", \"layer2.4.bn1.running_var\", \"layer2.4.conv2.weight\", \"layer2.4.bn2.weight\", \"layer2.4.bn2.bias\", \"layer2.4.bn2.running_mean\", \"layer2.4.bn2.running_var\", \"layer2.4.conv3.weight\", \"layer2.4.bn3.weight\", \"layer2.4.bn3.bias\", \"layer2.4.bn3.running_mean\", \"layer2.4.bn3.running_var\", \"layer2.4.se_module.fc1.weight\", \"layer2.4.se_module.fc1.bias\", \"layer2.4.se_module.fc2.weight\", \"layer2.4.se_module.fc2.bias\", \"layer2.5.conv1.weight\", \"layer2.5.bn1.weight\", \"layer2.5.bn1.bias\", \"layer2.5.bn1.running_mean\", \"layer2.5.bn1.running_var\", \"layer2.5.conv2.weight\", \"layer2.5.bn2.weight\", \"layer2.5.bn2.bias\", \"layer2.5.bn2.running_mean\", \"layer2.5.bn2.running_var\", \"layer2.5.conv3.weight\", \"layer2.5.bn3.weight\", \"layer2.5.bn3.bias\", \"layer2.5.bn3.running_mean\", \"layer2.5.bn3.running_var\", \"layer2.5.se_module.fc1.weight\", \"layer2.5.se_module.fc1.bias\", \"layer2.5.se_module.fc2.weight\", \"layer2.5.se_module.fc2.bias\", \"layer2.6.conv1.weight\", \"layer2.6.bn1.weight\", \"layer2.6.bn1.bias\", \"layer2.6.bn1.running_mean\", \"layer2.6.bn1.running_var\", \"layer2.6.conv2.weight\", \"layer2.6.bn2.weight\", \"layer2.6.bn2.bias\", \"layer2.6.bn2.running_mean\", \"layer2.6.bn2.running_var\", \"layer2.6.conv3.weight\", \"layer2.6.bn3.weight\", \"layer2.6.bn3.bias\", \"layer2.6.bn3.running_mean\", \"layer2.6.bn3.running_var\", \"layer2.6.se_module.fc1.weight\", \"layer2.6.se_module.fc1.bias\", \"layer2.6.se_module.fc2.weight\", \"layer2.6.se_module.fc2.bias\", 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\"layer3.35.bn3.weight\", \"layer3.35.bn3.bias\", \"layer3.35.bn3.running_mean\", \"layer3.35.bn3.running_var\", \"layer3.35.se_module.fc1.weight\", \"layer3.35.se_module.fc1.bias\", \"layer3.35.se_module.fc2.weight\", \"layer3.35.se_module.fc2.bias\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.se_module.fc1.weight\", \"layer4.0.se_module.fc1.bias\", \"layer4.0.se_module.fc2.weight\", \"layer4.0.se_module.fc2.bias\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.se_module.fc1.weight\", \"layer4.1.se_module.fc1.bias\", \"layer4.1.se_module.fc2.weight\", \"layer4.1.se_module.fc2.bias\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.se_module.fc1.weight\", \"layer4.2.se_module.fc1.bias\", \"layer4.2.se_module.fc2.weight\", \"layer4.2.se_module.fc2.bias\", \"last_linear.weight\", \"last_linear.bias\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.weight\", \"bn1.bias\", \"fc.weight\", \"fc.bias\". \n\tsize mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 1, 1]).\n\tsize mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.0.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).\n\tsize mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-ddaf61da00f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msai\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mse_resnet152\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msai\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mse_resnet152\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpretrained\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\sai-0.0.2-py3.7.egg\\sai\\models\\senet.py\u001b[0m in \u001b[0;36mse_resnet152\u001b[1;34m(num_classes, pretrained)\u001b[0m\n\u001b[0;32m    423\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    424\u001b[0m         \u001b[0msettings\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpretrained_settings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'se_resnet152'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'imagenet'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 425\u001b[1;33m         \u001b[0minitialize_pretrained_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    426\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\sai-0.0.2-py3.7.egg\\sai\\models\\senet.py\u001b[0m in \u001b[0;36minitialize_pretrained_model\u001b[1;34m(model, num_classes, settings)\u001b[0m\n\u001b[0;32m    377\u001b[0m         'num_classes should be {}, but is {}'.format(\n\u001b[0;32m    378\u001b[0m             settings['num_classes'], num_classes)\n\u001b[1;32m--> 379\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'url'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    380\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_space\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input_space'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input_size'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[1;34m(self, state_dict, strict)\u001b[0m\n\u001b[0;32m    828\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    829\u001b[0m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[1;32m--> 830\u001b[1;33m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[0m\u001b[0;32m    831\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_IncompatibleKeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munexpected_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for SENet:\n\tMissing key(s) in state_dict: \"layer0.conv1.weight\", \"layer0.bn1.weight\", \"layer0.bn1.bias\", \"layer0.bn1.running_mean\", \"layer0.bn1.running_var\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.se_module.fc1.weight\", \"layer1.0.se_module.fc1.bias\", \"layer1.0.se_module.fc2.weight\", \"layer1.0.se_module.fc2.bias\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.se_module.fc1.weight\", \"layer1.1.se_module.fc1.bias\", \"layer1.1.se_module.fc2.weight\", \"layer1.1.se_module.fc2.bias\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.se_module.fc1.weight\", \"layer1.2.se_module.fc1.bias\", \"layer1.2.se_module.fc2.weight\", \"layer1.2.se_module.fc2.bias\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.se_module.fc1.weight\", \"layer2.0.se_module.fc1.bias\", \"layer2.0.se_module.fc2.weight\", \"layer2.0.se_module.fc2.bias\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.se_module.fc1.weight\", \"layer2.1.se_module.fc1.bias\", \"layer2.1.se_module.fc2.weight\", \"layer2.1.se_module.fc2.bias\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.se_module.fc1.weight\", \"layer2.2.se_module.fc1.bias\", \"layer2.2.se_module.fc2.weight\", \"layer2.2.se_module.fc2.bias\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.se_module.fc1.weight\", \"layer2.3.se_module.fc1.bias\", \"layer2.3.se_module.fc2.weight\", \"layer2.3.se_module.fc2.bias\", \"layer2.4.conv1.weight\", \"layer2.4.bn1.weight\", \"layer2.4.bn1.bias\", \"layer2.4.bn1.running_mean\", \"layer2.4.bn1.running_var\", \"layer2.4.conv2.weight\", \"layer2.4.bn2.weight\", \"layer2.4.bn2.bias\", \"layer2.4.bn2.running_mean\", \"layer2.4.bn2.running_var\", \"layer2.4.conv3.weight\", \"layer2.4.bn3.weight\", \"layer2.4.bn3.bias\", \"layer2.4.bn3.running_mean\", \"layer2.4.bn3.running_var\", \"layer2.4.se_module.fc1.weight\", \"layer2.4.se_module.fc1.bias\", \"layer2.4.se_module.fc2.weight\", \"layer2.4.se_module.fc2.bias\", \"layer2.5.conv1.weight\", \"layer2.5.bn1.weight\", \"layer2.5.bn1.bias\", \"layer2.5.bn1.running_mean\", 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\"layer3.33.se_module.fc1.bias\", \"layer3.33.se_module.fc2.weight\", \"layer3.33.se_module.fc2.bias\", \"layer3.34.conv1.weight\", \"layer3.34.bn1.weight\", \"layer3.34.bn1.bias\", \"layer3.34.bn1.running_mean\", \"layer3.34.bn1.running_var\", \"layer3.34.conv2.weight\", \"layer3.34.bn2.weight\", \"layer3.34.bn2.bias\", \"layer3.34.bn2.running_mean\", \"layer3.34.bn2.running_var\", \"layer3.34.conv3.weight\", \"layer3.34.bn3.weight\", \"layer3.34.bn3.bias\", \"layer3.34.bn3.running_mean\", \"layer3.34.bn3.running_var\", \"layer3.34.se_module.fc1.weight\", \"layer3.34.se_module.fc1.bias\", \"layer3.34.se_module.fc2.weight\", \"layer3.34.se_module.fc2.bias\", \"layer3.35.conv1.weight\", \"layer3.35.bn1.weight\", \"layer3.35.bn1.bias\", \"layer3.35.bn1.running_mean\", \"layer3.35.bn1.running_var\", \"layer3.35.conv2.weight\", \"layer3.35.bn2.weight\", \"layer3.35.bn2.bias\", \"layer3.35.bn2.running_mean\", \"layer3.35.bn2.running_var\", \"layer3.35.conv3.weight\", \"layer3.35.bn3.weight\", \"layer3.35.bn3.bias\", \"layer3.35.bn3.running_mean\", \"layer3.35.bn3.running_var\", \"layer3.35.se_module.fc1.weight\", \"layer3.35.se_module.fc1.bias\", \"layer3.35.se_module.fc2.weight\", \"layer3.35.se_module.fc2.bias\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.se_module.fc1.weight\", \"layer4.0.se_module.fc1.bias\", \"layer4.0.se_module.fc2.weight\", \"layer4.0.se_module.fc2.bias\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.se_module.fc1.weight\", \"layer4.1.se_module.fc1.bias\", \"layer4.1.se_module.fc2.weight\", \"layer4.1.se_module.fc2.bias\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.se_module.fc1.weight\", \"layer4.2.se_module.fc1.bias\", \"layer4.2.se_module.fc2.weight\", \"layer4.2.se_module.fc2.bias\", \"last_linear.weight\", \"last_linear.bias\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.weight\", \"bn1.bias\", \"fc.weight\", \"fc.bias\". \n\tsize mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 1, 1]).\n\tsize mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).\n\tsize mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).\n\tsize mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).\n\tsize mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.0.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).\n\tsize mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1])."
     ]
    }
   ],
   "source": [
    "model = sai.models.se_resnet152()\n",
    "model = sai.models.se_resnet152(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Error(s) in loading state_dict for SENet:\n\tMissing key(s) in state_dict: \"layer0.conv1.weight\", \"layer0.bn1.weight\", \"layer0.bn1.bias\", \"layer0.bn1.running_mean\", \"layer0.bn1.running_var\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.se_module.fc1.weight\", \"layer1.0.se_module.fc1.bias\", \"layer1.0.se_module.fc2.weight\", \"layer1.0.se_module.fc2.bias\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.se_module.fc1.weight\", \"layer1.1.se_module.fc1.bias\", \"layer1.1.se_module.fc2.weight\", \"layer1.1.se_module.fc2.bias\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.se_module.fc1.weight\", \"layer1.2.se_module.fc1.bias\", \"layer1.2.se_module.fc2.weight\", \"layer1.2.se_module.fc2.bias\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.se_module.fc1.weight\", \"layer2.0.se_module.fc1.bias\", \"layer2.0.se_module.fc2.weight\", \"layer2.0.se_module.fc2.bias\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.se_module.fc1.weight\", \"layer2.1.se_module.fc1.bias\", \"layer2.1.se_module.fc2.weight\", \"layer2.1.se_module.fc2.bias\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.se_module.fc1.weight\", \"layer2.2.se_module.fc1.bias\", \"layer2.2.se_module.fc2.weight\", \"layer2.2.se_module.fc2.bias\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.se_module.fc1.weight\", \"layer2.3.se_module.fc1.bias\", \"layer2.3.se_module.fc2.weight\", \"layer2.3.se_module.fc2.bias\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.se_module.fc1.weight\", \"layer3.0.se_module.fc1.bias\", \"layer3.0.se_module.fc2.weight\", \"layer3.0.se_module.fc2.bias\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.se_module.fc1.weight\", \"layer3.1.se_module.fc1.bias\", \"layer3.1.se_module.fc2.weight\", \"layer3.1.se_module.fc2.bias\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.se_module.fc1.weight\", \"layer3.2.se_module.fc1.bias\", \"layer3.2.se_module.fc2.weight\", \"layer3.2.se_module.fc2.bias\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.se_module.fc1.weight\", \"layer3.3.se_module.fc1.bias\", \"layer3.3.se_module.fc2.weight\", \"layer3.3.se_module.fc2.bias\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.se_module.fc1.weight\", \"layer3.4.se_module.fc1.bias\", \"layer3.4.se_module.fc2.weight\", \"layer3.4.se_module.fc2.bias\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.se_module.fc1.weight\", \"layer3.5.se_module.fc1.bias\", \"layer3.5.se_module.fc2.weight\", \"layer3.5.se_module.fc2.bias\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.se_module.fc1.weight\", \"layer4.0.se_module.fc1.bias\", \"layer4.0.se_module.fc2.weight\", \"layer4.0.se_module.fc2.bias\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.se_module.fc1.weight\", \"layer4.1.se_module.fc1.bias\", \"layer4.1.se_module.fc2.weight\", \"layer4.1.se_module.fc2.bias\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.se_module.fc1.weight\", \"layer4.2.se_module.fc1.bias\", \"layer4.2.se_module.fc2.weight\", \"layer4.2.se_module.fc2.bias\", \"last_linear.weight\", \"last_linear.bias\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.weight\", \"bn1.bias\", \"fc.weight\", \"fc.bias\". \n\tsize mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 64, 1, 1]).\n\tsize mismatch for layer1.0.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 4, 3, 3]).\n\tsize mismatch for layer1.0.bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer1.1.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 4, 3, 3]).\n\tsize mismatch for layer1.1.bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer1.2.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 4, 3, 3]).\n\tsize mismatch for layer1.2.bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).\n\tsize mismatch for layer2.0.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.0.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer2.1.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.1.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer2.2.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.2.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer2.3.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.3.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).\n\tsize mismatch for layer3.0.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.0.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.1.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.1.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.2.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.2.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.3.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.3.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.4.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.4.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.5.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.5.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 1, 1]).\n\tsize mismatch for layer4.0.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 32, 3, 3]).\n\tsize mismatch for layer4.0.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.downsample.0.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 2048, 1, 1]).\n\tsize mismatch for layer4.1.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 32, 3, 3]).\n\tsize mismatch for layer4.1.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 2048, 1, 1]).\n\tsize mismatch for layer4.2.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 32, 3, 3]).\n\tsize mismatch for layer4.2.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-9b3a42a65d90>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msai\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mse_resnext50_32x4d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m# model = sai.models.se_resnext50_32x4d(pretrained=True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\sai-0.0.2-py3.7.egg\\sai\\models\\senet.py\u001b[0m in \u001b[0;36mse_resnext50_32x4d\u001b[1;34m(num_classes, pretrained)\u001b[0m\n\u001b[0;32m    434\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpretrained\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m         \u001b[0msettings\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpretrained_settings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'se_resnext50_32x4d'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'imagenet'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m         \u001b[0minitialize_pretrained_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    437\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\sai-0.0.2-py3.7.egg\\sai\\models\\senet.py\u001b[0m in \u001b[0;36minitialize_pretrained_model\u001b[1;34m(model, num_classes, settings)\u001b[0m\n\u001b[0;32m    377\u001b[0m         'num_classes should be {}, but is {}'.format(\n\u001b[0;32m    378\u001b[0m             settings['num_classes'], num_classes)\n\u001b[1;32m--> 379\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'url'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    380\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_space\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input_space'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msettings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'input_size'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\soft\\Anaconda3\\envs\\test\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[1;34m(self, state_dict, strict)\u001b[0m\n\u001b[0;32m    828\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    829\u001b[0m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[1;32m--> 830\u001b[1;33m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[0m\u001b[0;32m    831\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_IncompatibleKeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmissing_keys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munexpected_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    832\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for SENet:\n\tMissing key(s) in state_dict: \"layer0.conv1.weight\", \"layer0.bn1.weight\", \"layer0.bn1.bias\", \"layer0.bn1.running_mean\", \"layer0.bn1.running_var\", \"layer1.0.conv3.weight\", \"layer1.0.bn3.weight\", \"layer1.0.bn3.bias\", \"layer1.0.bn3.running_mean\", \"layer1.0.bn3.running_var\", \"layer1.0.se_module.fc1.weight\", \"layer1.0.se_module.fc1.bias\", \"layer1.0.se_module.fc2.weight\", \"layer1.0.se_module.fc2.bias\", \"layer1.0.downsample.0.weight\", \"layer1.0.downsample.1.weight\", \"layer1.0.downsample.1.bias\", \"layer1.0.downsample.1.running_mean\", \"layer1.0.downsample.1.running_var\", \"layer1.1.conv3.weight\", \"layer1.1.bn3.weight\", \"layer1.1.bn3.bias\", \"layer1.1.bn3.running_mean\", \"layer1.1.bn3.running_var\", \"layer1.1.se_module.fc1.weight\", \"layer1.1.se_module.fc1.bias\", \"layer1.1.se_module.fc2.weight\", \"layer1.1.se_module.fc2.bias\", \"layer1.2.conv3.weight\", \"layer1.2.bn3.weight\", \"layer1.2.bn3.bias\", \"layer1.2.bn3.running_mean\", \"layer1.2.bn3.running_var\", \"layer1.2.se_module.fc1.weight\", \"layer1.2.se_module.fc1.bias\", \"layer1.2.se_module.fc2.weight\", \"layer1.2.se_module.fc2.bias\", \"layer2.0.conv3.weight\", \"layer2.0.bn3.weight\", \"layer2.0.bn3.bias\", \"layer2.0.bn3.running_mean\", \"layer2.0.bn3.running_var\", \"layer2.0.se_module.fc1.weight\", \"layer2.0.se_module.fc1.bias\", \"layer2.0.se_module.fc2.weight\", \"layer2.0.se_module.fc2.bias\", \"layer2.1.conv3.weight\", \"layer2.1.bn3.weight\", \"layer2.1.bn3.bias\", \"layer2.1.bn3.running_mean\", \"layer2.1.bn3.running_var\", \"layer2.1.se_module.fc1.weight\", \"layer2.1.se_module.fc1.bias\", \"layer2.1.se_module.fc2.weight\", \"layer2.1.se_module.fc2.bias\", \"layer2.2.conv3.weight\", \"layer2.2.bn3.weight\", \"layer2.2.bn3.bias\", \"layer2.2.bn3.running_mean\", \"layer2.2.bn3.running_var\", \"layer2.2.se_module.fc1.weight\", \"layer2.2.se_module.fc1.bias\", \"layer2.2.se_module.fc2.weight\", \"layer2.2.se_module.fc2.bias\", \"layer2.3.conv3.weight\", \"layer2.3.bn3.weight\", \"layer2.3.bn3.bias\", \"layer2.3.bn3.running_mean\", \"layer2.3.bn3.running_var\", \"layer2.3.se_module.fc1.weight\", \"layer2.3.se_module.fc1.bias\", \"layer2.3.se_module.fc2.weight\", \"layer2.3.se_module.fc2.bias\", \"layer3.0.conv3.weight\", \"layer3.0.bn3.weight\", \"layer3.0.bn3.bias\", \"layer3.0.bn3.running_mean\", \"layer3.0.bn3.running_var\", \"layer3.0.se_module.fc1.weight\", \"layer3.0.se_module.fc1.bias\", \"layer3.0.se_module.fc2.weight\", \"layer3.0.se_module.fc2.bias\", \"layer3.1.conv3.weight\", \"layer3.1.bn3.weight\", \"layer3.1.bn3.bias\", \"layer3.1.bn3.running_mean\", \"layer3.1.bn3.running_var\", \"layer3.1.se_module.fc1.weight\", \"layer3.1.se_module.fc1.bias\", \"layer3.1.se_module.fc2.weight\", \"layer3.1.se_module.fc2.bias\", \"layer3.2.conv3.weight\", \"layer3.2.bn3.weight\", \"layer3.2.bn3.bias\", \"layer3.2.bn3.running_mean\", \"layer3.2.bn3.running_var\", \"layer3.2.se_module.fc1.weight\", \"layer3.2.se_module.fc1.bias\", \"layer3.2.se_module.fc2.weight\", \"layer3.2.se_module.fc2.bias\", \"layer3.3.conv3.weight\", \"layer3.3.bn3.weight\", \"layer3.3.bn3.bias\", \"layer3.3.bn3.running_mean\", \"layer3.3.bn3.running_var\", \"layer3.3.se_module.fc1.weight\", \"layer3.3.se_module.fc1.bias\", \"layer3.3.se_module.fc2.weight\", \"layer3.3.se_module.fc2.bias\", \"layer3.4.conv3.weight\", \"layer3.4.bn3.weight\", \"layer3.4.bn3.bias\", \"layer3.4.bn3.running_mean\", \"layer3.4.bn3.running_var\", \"layer3.4.se_module.fc1.weight\", \"layer3.4.se_module.fc1.bias\", \"layer3.4.se_module.fc2.weight\", \"layer3.4.se_module.fc2.bias\", \"layer3.5.conv3.weight\", \"layer3.5.bn3.weight\", \"layer3.5.bn3.bias\", \"layer3.5.bn3.running_mean\", \"layer3.5.bn3.running_var\", \"layer3.5.se_module.fc1.weight\", \"layer3.5.se_module.fc1.bias\", \"layer3.5.se_module.fc2.weight\", \"layer3.5.se_module.fc2.bias\", \"layer4.0.conv3.weight\", \"layer4.0.bn3.weight\", \"layer4.0.bn3.bias\", \"layer4.0.bn3.running_mean\", \"layer4.0.bn3.running_var\", \"layer4.0.se_module.fc1.weight\", \"layer4.0.se_module.fc1.bias\", \"layer4.0.se_module.fc2.weight\", \"layer4.0.se_module.fc2.bias\", \"layer4.1.conv3.weight\", \"layer4.1.bn3.weight\", \"layer4.1.bn3.bias\", \"layer4.1.bn3.running_mean\", \"layer4.1.bn3.running_var\", \"layer4.1.se_module.fc1.weight\", \"layer4.1.se_module.fc1.bias\", \"layer4.1.se_module.fc2.weight\", \"layer4.1.se_module.fc2.bias\", \"layer4.2.conv3.weight\", \"layer4.2.bn3.weight\", \"layer4.2.bn3.bias\", \"layer4.2.bn3.running_mean\", \"layer4.2.bn3.running_var\", \"layer4.2.se_module.fc1.weight\", \"layer4.2.se_module.fc1.bias\", \"layer4.2.se_module.fc2.weight\", \"layer4.2.se_module.fc2.bias\", \"last_linear.weight\", \"last_linear.bias\". \n\tUnexpected key(s) in state_dict: \"conv1.weight\", \"bn1.running_mean\", \"bn1.running_var\", \"bn1.weight\", \"bn1.bias\", \"fc.weight\", \"fc.bias\". \n\tsize mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 64, 1, 1]).\n\tsize mismatch for layer1.0.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 4, 3, 3]).\n\tsize mismatch for layer1.0.bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.0.bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer1.1.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 4, 3, 3]).\n\tsize mismatch for layer1.1.bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.1.bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).\n\tsize mismatch for layer1.2.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.conv2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 4, 3, 3]).\n\tsize mismatch for layer1.2.bn2.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn2.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer1.2.bn2.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).\n\tsize mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([128, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).\n\tsize mismatch for layer2.0.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.0.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.0.downsample.0.weight: copying a param with shape torch.Size([128, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 256, 1, 1]).\n\tsize mismatch for layer2.0.downsample.1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.0.downsample.1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer2.1.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.1.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.1.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer2.2.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.2.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.2.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).\n\tsize mismatch for layer2.3.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.conv2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 8, 3, 3]).\n\tsize mismatch for layer2.3.bn2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn2.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer2.3.bn2.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).\n\tsize mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([256, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 512, 1, 1]).\n\tsize mismatch for layer3.0.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.0.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.0.downsample.0.weight: copying a param with shape torch.Size([256, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 512, 1, 1]).\n\tsize mismatch for layer3.0.downsample.1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.0.downsample.1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.1.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.1.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.1.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.2.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.2.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.2.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.3.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.3.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.3.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.4.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.4.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.4.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).\n\tsize mismatch for layer3.5.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.conv2.weight: copying a param with shape torch.Size([256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 16, 3, 3]).\n\tsize mismatch for layer3.5.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer3.5.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).\n\tsize mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([512, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 1024, 1, 1]).\n\tsize mismatch for layer4.0.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 32, 3, 3]).\n\tsize mismatch for layer4.0.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.0.downsample.0.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 1024, 1, 1]).\n\tsize mismatch for layer4.0.downsample.1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.0.downsample.1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([2048]).\n\tsize mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 2048, 1, 1]).\n\tsize mismatch for layer4.1.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 32, 3, 3]).\n\tsize mismatch for layer4.1.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.1.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 2048, 1, 1]).\n\tsize mismatch for layer4.2.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.conv2.weight: copying a param with shape torch.Size([512, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1024, 32, 3, 3]).\n\tsize mismatch for layer4.2.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024]).\n\tsize mismatch for layer4.2.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([1024])."
     ]
    }
   ],
   "source": [
    "model = sai.models.se_resnext50_32x4d()\n",
    "# model = sai.models.se_resnext50_32x4d(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.se_resnext101_32x4d()\n",
    "# model = sai.models.se_resnext101_32x4d(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sai.models.senet154()\n",
    "model = sai.models.senet154(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1000])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(torch.rand(1,3,288,288)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('se_resnet50-ce0d4300.pth', 'total size: ', 107, 'MB')\n",
      "[####################################              ] 72%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#########################################         ] 82%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[###########################################       ] 86%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[#############################################     ] 90%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[###############################################   ] 94%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[################################################# ] 99%"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 必须使用该方法下载模型，然后加载\n",
    "from flyai.utils import remote_helper\n",
    "path = remote_helper.get_remote_date('https://www.flyai.com/m/se_resnet50-ce0d4300.pth')"
   ]
  }
 ],
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   "title_sidebar": "Contents",
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